indicner-tamil-ExentAI

This model is a fine-tuned version of ai4bharat/IndicNER on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 0.1632
• Precision: 0.6005
• Recall: 0.7003
• F1: 0.6466
• Accuracy: 0.9624
• F1 Per: 0.6897
• Precision Per: 0.6481
• Recall Per: 0.7368
• F1 Loc: 0.7113
• Precision Loc: 0.6646
• Recall Loc: 0.7652
• F1 Org: 0.4625
• Precision Org: 0.4190
• Recall Org: 0.5161

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 6.857179151838835e-05
• train_batch_size: 64
• eval_batch_size: 64
• seed: 42
• optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 0.13646586401400382
• num_epochs: 10
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy	F1 Per	Precision Per	Recall Per	F1 Loc	Precision Loc	Recall Loc	F1 Org	Precision Org	Recall Org
0.4201	1.0	132	0.2198	0.2407	0.3604	0.2886	0.9252	0.2605	0.2394	0.2857	0.3803	0.3033	0.5097	0.0286	0.0244	0.0346
0.1555	2.0	264	0.1483	0.4512	0.5869	0.5102	0.9498	0.4578	0.4136	0.5126	0.6416	0.5649	0.7425	0.2059	0.1791	0.2421
0.1010	3.0	396	0.1445	0.4723	0.6263	0.5385	0.9531	0.4713	0.4109	0.5525	0.6770	0.6155	0.7521	0.2826	0.2294	0.3679
0.0600	4.0	528	0.1481	0.5467	0.6408	0.5900	0.9585	0.5315	0.4937	0.5756	0.7049	0.6745	0.7382	0.3760	0.3214	0.4528
0.0411	5.0	660	0.1546	0.5680	0.6605	0.6108	0.9611	0.5448	0.4860	0.6197	0.7189	0.6922	0.7479	0.4163	0.3766	0.4654
0.0298	6.0	792	0.1730	0.5534	0.6929	0.6154	0.9591	0.5714	0.5116	0.6471	0.7100	0.6442	0.7908	0.4120	0.3639	0.4748
0.0199	7.0	924	0.1812	0.5778	0.6842	0.6265	0.9612	0.5787	0.5277	0.6408	0.7305	0.6885	0.7779	0.4131	0.3656	0.4748
0.0170	8.0	1056	0.1914	0.5863	0.7005	0.6383	0.9608	0.5806	0.5265	0.6471	0.7398	0.6943	0.7918	0.4454	0.3937	0.5126
0.0138	9.0	1188	0.1975	0.5824	0.6999	0.6358	0.9608	0.5833	0.5281	0.6513	0.7334	0.6870	0.7865	0.4472	0.3929	0.5189
0.0120	10.0	1320	0.1998	0.5800	0.6976	0.6334	0.9607	0.5805	0.5236	0.6513	0.7316	0.6871	0.7822	0.4453	0.3901	0.5189

Framework versions

• Transformers 5.12.1
• Pytorch 2.11.0+cu128
• Datasets 4.0.0
• Tokenizers 0.22.2